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Hybrid AI-based Voice Authentication

Year 2023, , 17 - 22, 23.12.2023
https://doi.org/10.34110/forecasting.1260073

Abstract

Biometric authentication systems reveal individuals' physical or behavioral uniqueness and identify them by comparing them with existing records. Today, many biometric recognition systems, such as fingerprint reading, palm reading, and face reading, are being studied and used. The human voice is also among the techniques used for this purpose. Due to this feature, the human voice performs secure transactions and authentication in various fields. Based on these voice features, we used a dataset of 66,569 voice recordings. The voice recordings were revised to include six sentences of at least six words each from 24 different people to get the maximum benefit from the dataset. The voices in the reduced dataset were labeled as sentences belonging to the same person and sentences belonging to different people and converted into matrix form. A biometric recognition study resulted in a correlation score of 0.88. As a result of these processes, the feasibility of a voice biometric recognition system with artificial intelligence has been demonstrated.

References

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  • K. Fatima, S. Nawaz, and S. Mehrban, “Biometric Authentication in Health Care Sector: A Survey,” 3rd International Conference on Innovative Computing, ICIC 2019, Nov. 2019, doi: 10.1109/ICIC48496.2019.8966699.C. Berghoff, M. Neu, and A. von Twickel, “The Interplay of AI and Biometrics: Challenges and Opportunities,” Computer (Long Beach Calif), vol. 54, no. 09, pp. 80–85, Sep. 2021, doi: 10.1109/MC.2021.3084656.
  • C. Berghoff, M. Neu, and A. von Twickel, “Vulnerabilities of Connectionist AI Applications: Evaluation and Defense,” Front Big Data, vol. 3, p. 544373, Jul. 2020, doi: 10.3389/FDATA.2020.00023/BIBTEXA. Boles and P. Rad, “Voice Biometrics: Deep Learning-based Voiceprint Authentication System,” 2017, doi: 10.1109/SYSOSE.2017.7994971.
  • S. Albalawi, L. Alshahrani, N. Albalawi, R. Kilabi, and A. Alhakamy, “A Comprehensive Overview on Biometric Authentication Systems using Artificial Intelligence Techniques,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 4, pp. 782– 791, 2022, doi: 10.14569/IJACSA.2022.0130491.
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  • F. Alcantud, I. Dolz, C. Gaya, and M. Martín, “The voice recognition system as a way of accessing the computer for people with physical standards as usual,” Technol Disabil, vol. 18, no. 3, pp. 89–97, 2006, doi: 10.3233/TAD-2006- 18301.
  • A. Boles and P. Rad, “Voice biometrics: Deep learning-based voiceprint authentication system,” 2017 12th System of Systems Engineering Conference, SoSE 2017, Jul. 2017, doi: 10.1109/SYSOSE.2017.7994971.
  • H. H. Zhu, Q. H. He, H. Tang, and W. H. Cao, “Voiceprint-biometric template design and authentication based on cloud computing security,” 2011 International Conference on Cloud and Service Computing, pp. 302–308, 2011, doi: 10.1109/CSC.2011.6138538.
  • "Common Voice.” https://commonvoice.mozilla.org/en/datasets (accessed Dec. 20, 2022).
  • H. Shahid, S. Aziz, A. Aymin, M. U. Khan, and A. N. Remete, “A Survey on AI-based ECG, PPG, and PCG Signals Based Biometric Authentication System; A Survey on AI-based ECG, PPG, and PCG Signals Based Biometric Authentication System,” 2021, doi: 10.1109/ICECube53880.2021.9628307.
  • C. Berghoff, M. Neu, and A. von Twickel, “Vulnerabilities of Connectionist AI Applications: Evaluation and Defense,” Front Big Data, vol. 3, p. 23, Jul. 2020, doi: 10.3389/FDATA.2020.00023/BIBTEX.
  • S. B. Sadkhan, B. K. Al-Shukur, and A. K. Mattar, “Biometric voice authentication autoevaluation system,” 2017 Annual Conference on New Trends in Information and Communications Technology Applications, NTICT 2017, pp. 174– 179, Jul. 2017, doi: 10.1109/NTICT.2017.7976100.
  • S. B. Sadkhan, B. K. Al-Shukur, and A. K. Mattar, “Biometric voice authentication autoevaluation system,” 2017 Annual Conference on New Trends in Information and Communications Technology Applications, NTICT 2017, pp. 174– 179, Jul. 2017, doi: 10.1109/NTICT.2017.7976100.
  • J. Gałka, M. Masior and M. Salasa, "Voice authentication embedded solution for secured access control," in IEEE Transactions on Consumer Electronics, vol. 60, no. 4, pp. 653-661, Nov. 2014, doi: 10.1109/TCE.2014.7027339.
Year 2023, , 17 - 22, 23.12.2023
https://doi.org/10.34110/forecasting.1260073

Abstract

References

  • M. Nizam Kamarudin, H. Nizam Mohd Shah, M. Zamzuri Ab Rashid, M. Fairus Abdollah, C. Kok Lin, and Z. Kamis, “Biometric Voice Recognition in Security System,” 2014, Accessed: Aug. 28, 2023.
  • K. Fatima, S. Nawaz, and S. Mehrban, “Biometric Authentication in Health Care Sector: A Survey,” 3rd International Conference on Innovative Computing, ICIC 2019, Nov. 2019, doi: 10.1109/ICIC48496.2019.8966699.C. Berghoff, M. Neu, and A. von Twickel, “The Interplay of AI and Biometrics: Challenges and Opportunities,” Computer (Long Beach Calif), vol. 54, no. 09, pp. 80–85, Sep. 2021, doi: 10.1109/MC.2021.3084656.
  • C. Berghoff, M. Neu, and A. von Twickel, “Vulnerabilities of Connectionist AI Applications: Evaluation and Defense,” Front Big Data, vol. 3, p. 544373, Jul. 2020, doi: 10.3389/FDATA.2020.00023/BIBTEXA. Boles and P. Rad, “Voice Biometrics: Deep Learning-based Voiceprint Authentication System,” 2017, doi: 10.1109/SYSOSE.2017.7994971.
  • S. Albalawi, L. Alshahrani, N. Albalawi, R. Kilabi, and A. Alhakamy, “A Comprehensive Overview on Biometric Authentication Systems using Artificial Intelligence Techniques,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 4, pp. 782– 791, 2022, doi: 10.14569/IJACSA.2022.0130491.
  • J. Noyes and C. Frankish, “Speech recognition technology for individuals with disabilities,” Augmentative and Alternative Communication, vol. 8, no. 4, pp. 297–303, 1992.
  • F. Alcantud, I. Dolz, C. Gaya, and M. Martín, “The voice recognition system as a way of accessing the computer for people with physical standards as usual,” Technol Disabil, vol. 18, no. 3, pp. 89–97, 2006, doi: 10.3233/TAD-2006- 18301.
  • A. Boles and P. Rad, “Voice biometrics: Deep learning-based voiceprint authentication system,” 2017 12th System of Systems Engineering Conference, SoSE 2017, Jul. 2017, doi: 10.1109/SYSOSE.2017.7994971.
  • H. H. Zhu, Q. H. He, H. Tang, and W. H. Cao, “Voiceprint-biometric template design and authentication based on cloud computing security,” 2011 International Conference on Cloud and Service Computing, pp. 302–308, 2011, doi: 10.1109/CSC.2011.6138538.
  • "Common Voice.” https://commonvoice.mozilla.org/en/datasets (accessed Dec. 20, 2022).
  • H. Shahid, S. Aziz, A. Aymin, M. U. Khan, and A. N. Remete, “A Survey on AI-based ECG, PPG, and PCG Signals Based Biometric Authentication System; A Survey on AI-based ECG, PPG, and PCG Signals Based Biometric Authentication System,” 2021, doi: 10.1109/ICECube53880.2021.9628307.
  • C. Berghoff, M. Neu, and A. von Twickel, “Vulnerabilities of Connectionist AI Applications: Evaluation and Defense,” Front Big Data, vol. 3, p. 23, Jul. 2020, doi: 10.3389/FDATA.2020.00023/BIBTEX.
  • S. B. Sadkhan, B. K. Al-Shukur, and A. K. Mattar, “Biometric voice authentication autoevaluation system,” 2017 Annual Conference on New Trends in Information and Communications Technology Applications, NTICT 2017, pp. 174– 179, Jul. 2017, doi: 10.1109/NTICT.2017.7976100.
  • S. B. Sadkhan, B. K. Al-Shukur, and A. K. Mattar, “Biometric voice authentication autoevaluation system,” 2017 Annual Conference on New Trends in Information and Communications Technology Applications, NTICT 2017, pp. 174– 179, Jul. 2017, doi: 10.1109/NTICT.2017.7976100.
  • J. Gałka, M. Masior and M. Salasa, "Voice authentication embedded solution for secured access control," in IEEE Transactions on Consumer Electronics, vol. 60, no. 4, pp. 653-661, Nov. 2014, doi: 10.1109/TCE.2014.7027339.
There are 14 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Articles
Authors

Bilal Bora 0009-0005-2095-4869

Ahmet Emin Emanet 0000-0003-4642-8603

Enes Elmacı 0009-0005-0819-6233

Derya Kandaz 0000-0003-3067-4770

Muhammed Kürşad Uçar 0000-0002-0636-8645

Early Pub Date December 18, 2023
Publication Date December 23, 2023
Submission Date March 4, 2023
Acceptance Date December 17, 2023
Published in Issue Year 2023

Cite

APA Bora, B., Emanet, A. E., Elmacı, E., Kandaz, D., et al. (2023). Hybrid AI-based Voice Authentication. Turkish Journal of Forecasting, 07(2), 17-22. https://doi.org/10.34110/forecasting.1260073
AMA Bora B, Emanet AE, Elmacı E, Kandaz D, Uçar MK. Hybrid AI-based Voice Authentication. TJF. December 2023;07(2):17-22. doi:10.34110/forecasting.1260073
Chicago Bora, Bilal, Ahmet Emin Emanet, Enes Elmacı, Derya Kandaz, and Muhammed Kürşad Uçar. “Hybrid AI-Based Voice Authentication”. Turkish Journal of Forecasting 07, no. 2 (December 2023): 17-22. https://doi.org/10.34110/forecasting.1260073.
EndNote Bora B, Emanet AE, Elmacı E, Kandaz D, Uçar MK (December 1, 2023) Hybrid AI-based Voice Authentication. Turkish Journal of Forecasting 07 2 17–22.
IEEE B. Bora, A. E. Emanet, E. Elmacı, D. Kandaz, and M. K. Uçar, “Hybrid AI-based Voice Authentication”, TJF, vol. 07, no. 2, pp. 17–22, 2023, doi: 10.34110/forecasting.1260073.
ISNAD Bora, Bilal et al. “Hybrid AI-Based Voice Authentication”. Turkish Journal of Forecasting 07/2 (December 2023), 17-22. https://doi.org/10.34110/forecasting.1260073.
JAMA Bora B, Emanet AE, Elmacı E, Kandaz D, Uçar MK. Hybrid AI-based Voice Authentication. TJF. 2023;07:17–22.
MLA Bora, Bilal et al. “Hybrid AI-Based Voice Authentication”. Turkish Journal of Forecasting, vol. 07, no. 2, 2023, pp. 17-22, doi:10.34110/forecasting.1260073.
Vancouver Bora B, Emanet AE, Elmacı E, Kandaz D, Uçar MK. Hybrid AI-based Voice Authentication. TJF. 2023;07(2):17-22.

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